Identification of homologous series as organic tracers in PM samples: a signal processing method for studying complex GC-MS signals

Abstract

In the present paper, a signal processing procedure based on the AutoCovariance Function (ACVFtot) is applied to GC-MS signals of atmospheric aerosols. This is a computer-assisted signal processing procedure able to transform GC data into usable information by extracting all the analytical results hidden in the complex chromatogram. The method is further extended by deriving new mathematical equations and implementing a new computation algorithm to extract information on the homologous series -- nmax and CPI -- directly from the experimental EACVFtot computed on the acquired chromatographic signal. The procedure was validated on simulated chromatograms with known distribution of the terms of the series: the obtained results prove that the parameters nmax and CPI of the homologous series can be estimated with good accuracy and precision. The method was applied to experimental chromatograms of real samples: aerosol samples (PM2.5 and PM10) were collected daily in urban and rural sites. The information on distribution pattern of n-alkanes and n-alkanoic can be directly obtained from the EACVFtot computed on the acquired chromatogram, thus reducing the labour and data handling time and removing the subjective step of peak integration. The advantages of the method can be singled out by comparison with the traditional procedure based on GC peak identification and integration